A novel phase-unwrapping method by using phase-jump detection and local surface fitting: application to Dixon water-fat MRI.

Abstract

This study aims to develop an accurate and robust phase-unwrapping method that works effectively under severe noise, rapid-varying phase, and disconnected regions for water-fat Dixon MRI. The proposed method first segments the phase map into blocks by automatically detecting phase jumps, and then clusters the pixels near phase jumps into residual pixels. Thereafter, the proposed method sequentially performs intrablock, interblock, and residual-pixel unwrapping using the local surface fitting approach. To address intrablock wraps, the proposed method segments each block into subblocks using the phase partition approach and then performs inter-subblock unwrapping using a block-growing approach. The phase derivative variance is used as the quality criterion to determine the region-growing path of residual pixels. The performance of the proposed method was evaluated on simulation and in vivo Dixon data. The proposed method obtained accurate phase-unwrapping results in the simulation experiment with severe noise, rapid-varying phase, and disconnected regions, and the mean and SD error ratio was 0.26 ± 0.07%. For 505 in vivo knee and ankle images, the total water-fat swap ratio by the proposed method was 1.78%, whereas those by phase region expanding labeler for unwrapping discrete estimates and clustering and local surface fitting were 38.42% and 7.72%, respectively. The proposed method achieves accurate and robust performance in phase unwrapping and can benefit phase-related MRI applications such as Dixon water-fat separation.